{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook to perform prediction and plotting DeepExplainer by using DeepMEL\n",
    "#### Following codes in this notebook were run using conda environments\n",
    "#### Here are the used packages and their version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%%bash\n",
    "##cpu\n",
    "# conda create --name DeepMEL_conda_env_cpu python=3.6 tensorflow=1.14.0 keras=2.2.4\n",
    "# conda activate DeepMEL_conda_env_cpu\n",
    "# conda install numpy=1.16.2 matplotlib=3.1.1 shap=0.29.3 ipykernel=5.1.2\n",
    "\n",
    "# #gpu\n",
    "# conda create --name DeepMEL_conda_env_gpu python=3.6 tensorflow-gpu=1.14.0 keras-gpu=2.2.4\n",
    "# conda activate DeepMEL_conda_env_gpu\n",
    "# conda install numpy=1.16.2  matplotlib=3.1.1 shap=0.29.3 ipykernel=5.1.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading necessary packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0428 14:51:58.576709 47675164401280 deprecation_wrapper.py:119] From /staging/leuven/stg_00002/lcb/itask/programs/anaconda3/envs/deeplearning/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:153: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "import tempfile, sys, os\n",
    "from array import *\n",
    "sys.path.insert(0, os.path.abspath('..'))\n",
    "\n",
    "import keras\n",
    "from keras.models import Sequential, Model\n",
    "from keras.models import model_from_json\n",
    "from keras.layers import Dense, Dropout, Flatten, Activation, Bidirectional, Concatenate\n",
    "from keras.layers.convolutional import Conv1D, MaxPooling1D\n",
    "from keras.layers.recurrent import LSTM\n",
    "from keras.utils import to_categorical, plot_model\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras import backend as K\n",
    "K.set_learning_phase(False)\n",
    "\n",
    "import shap\n",
    "import shap.explainers.deep.deep_tf\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import sklearn\n",
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Defining necessary functitons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_a(ax, base, left_edge, height, color):\n",
    "    a_polygon_coords = [\n",
    "        np.array([\n",
    "           [0.0, 0.0],\n",
    "           [0.5, 1.0],\n",
    "           [0.5, 0.8],\n",
    "           [0.2, 0.0],\n",
    "        ]),\n",
    "        np.array([\n",
    "           [1.0, 0.0],\n",
    "           [0.5, 1.0],\n",
    "           [0.5, 0.8],\n",
    "           [0.8, 0.0],\n",
    "        ]),\n",
    "        np.array([\n",
    "           [0.225, 0.45],\n",
    "           [0.775, 0.45],\n",
    "           [0.85, 0.3],\n",
    "           [0.15, 0.3],\n",
    "        ])\n",
    "    ]\n",
    "    for polygon_coords in a_polygon_coords:\n",
    "        ax.add_patch(matplotlib.patches.Polygon((np.array([1,height])[None,:]*polygon_coords\n",
    "                                                 + np.array([left_edge,base])[None,:]),\n",
    "                                                facecolor=color, edgecolor=color))\n",
    "\n",
    "def plot_c(ax, base, left_edge, height, color):\n",
    "    ax.add_patch(matplotlib.patches.Ellipse(xy=[left_edge+0.65, base+0.5*height], width=1.3, height=height,\n",
    "                                            facecolor=color, edgecolor=color))\n",
    "    ax.add_patch(matplotlib.patches.Ellipse(xy=[left_edge+0.65, base+0.5*height], width=0.7*1.3, height=0.7*height,\n",
    "                                            facecolor='white', edgecolor='white'))\n",
    "    ax.add_patch(matplotlib.patches.Rectangle(xy=[left_edge+1, base], width=1.0, height=height,\n",
    "                                            facecolor='white', edgecolor='white', fill=True))\n",
    "\n",
    "def plot_g(ax, base, left_edge, height, color):\n",
    "    ax.add_patch(matplotlib.patches.Ellipse(xy=[left_edge+0.65, base+0.5*height], width=1.3, height=height,\n",
    "                                            facecolor=color, edgecolor=color))\n",
    "    ax.add_patch(matplotlib.patches.Ellipse(xy=[left_edge+0.65, base+0.5*height], width=0.7*1.3, height=0.7*height,\n",
    "                                            facecolor='white', edgecolor='white'))\n",
    "    ax.add_patch(matplotlib.patches.Rectangle(xy=[left_edge+1, base], width=1.0, height=height,\n",
    "                                            facecolor='white', edgecolor='white', fill=True))\n",
    "    ax.add_patch(matplotlib.patches.Rectangle(xy=[left_edge+0.825, base+0.085*height], width=0.174, height=0.415*height,\n",
    "                                            facecolor=color, edgecolor=color, fill=True))\n",
    "    ax.add_patch(matplotlib.patches.Rectangle(xy=[left_edge+0.625, base+0.35*height], width=0.374, height=0.15*height,\n",
    "                                            facecolor=color, edgecolor=color, fill=True))\n",
    "\n",
    "def plot_t(ax, base, left_edge, height, color):\n",
    "    ax.add_patch(matplotlib.patches.Rectangle(xy=[left_edge+0.4, base],\n",
    "                  width=0.2, height=height, facecolor=color, edgecolor=color, fill=True))\n",
    "    ax.add_patch(matplotlib.patches.Rectangle(xy=[left_edge, base+0.8*height],\n",
    "                  width=1.0, height=0.2*height, facecolor=color, edgecolor=color, fill=True))\n",
    "\n",
    "default_colors = {0:'green', 1:'blue', 2:'orange', 3:'red'}\n",
    "default_plot_funcs = {0:plot_a, 1:plot_c, 2:plot_g, 3:plot_t}\n",
    "def plot_weights_given_ax(ax, array,\n",
    "                 height_padding_factor,\n",
    "                 length_padding,\n",
    "                 subticks_frequency,\n",
    "                 highlight,\n",
    "                 colors=default_colors,\n",
    "                 plot_funcs=default_plot_funcs):\n",
    "    if len(array.shape)==3:\n",
    "        array = np.squeeze(array)\n",
    "    assert len(array.shape)==2, array.shape\n",
    "    if (array.shape[0]==4 and array.shape[1] != 4):\n",
    "        array = array.transpose(1,0)\n",
    "    assert array.shape[1]==4\n",
    "    max_pos_height = 0.0\n",
    "    min_neg_height = 0.0\n",
    "    heights_at_positions = []\n",
    "    depths_at_positions = []\n",
    "    for i in range(array.shape[0]):\n",
    "        acgt_vals = sorted(enumerate(array[i,:]), key=lambda x: abs(x[1]))\n",
    "        positive_height_so_far = 0.0\n",
    "        negative_height_so_far = 0.0\n",
    "        for letter in acgt_vals:\n",
    "            plot_func = plot_funcs[letter[0]]\n",
    "            color=colors[letter[0]]\n",
    "            if (letter[1] > 0):\n",
    "                height_so_far = positive_height_so_far\n",
    "                positive_height_so_far += letter[1]                \n",
    "            else:\n",
    "                height_so_far = negative_height_so_far\n",
    "                negative_height_so_far += letter[1]\n",
    "            plot_func(ax=ax, base=height_so_far, left_edge=i, height=letter[1], color=color)\n",
    "        max_pos_height = max(max_pos_height, positive_height_so_far)\n",
    "        min_neg_height = min(min_neg_height, negative_height_so_far)\n",
    "        heights_at_positions.append(positive_height_so_far)\n",
    "        depths_at_positions.append(negative_height_so_far)\n",
    "    for color in highlight:\n",
    "        for start_pos, end_pos in highlight[color]:\n",
    "            assert start_pos >= 0.0 and end_pos <= array.shape[0]\n",
    "            min_depth = np.min(depths_at_positions[start_pos:end_pos])\n",
    "            max_height = np.max(heights_at_positions[start_pos:end_pos])\n",
    "            ax.add_patch(\n",
    "                matplotlib.patches.Rectangle(xy=[start_pos,min_depth],\n",
    "                    width=end_pos-start_pos,\n",
    "                    height=max_height-min_depth,\n",
    "                    edgecolor=color, fill=False))\n",
    "            \n",
    "    ax.set_xlim(-length_padding, array.shape[0]+length_padding)\n",
    "    ax.xaxis.set_ticks(np.arange(0.0, array.shape[0]+1, subticks_frequency))\n",
    "    height_padding = max(abs(min_neg_height)*(height_padding_factor),\n",
    "                         abs(max_pos_height)*(height_padding_factor))\n",
    "    ax.set_ylim(min_neg_height-height_padding, max_pos_height+height_padding)\n",
    "    return ax\n",
    "\n",
    "def plot_weights_modified(array, fig, n,n1,n2, title='', ylab='',\n",
    "                              figsize=(20,2),\n",
    "                 height_padding_factor=0.2,\n",
    "                 length_padding=1.0,\n",
    "                 subticks_frequency=20,\n",
    "                 colors=default_colors,\n",
    "                 plot_funcs=default_plot_funcs,\n",
    "                 highlight={}):\n",
    "    ax = fig.add_subplot(n,n1,n2) \n",
    "    ax.set_title(title)\n",
    "    ax.set_ylabel(ylab)\n",
    "    y = plot_weights_given_ax(ax=ax, array=array,\n",
    "        height_padding_factor=height_padding_factor,\n",
    "        length_padding=length_padding,\n",
    "        subticks_frequency=subticks_frequency,\n",
    "        colors=colors,\n",
    "        plot_funcs=plot_funcs,\n",
    "        highlight=highlight)\n",
    "    return fig,ax\n",
    "\n",
    "def json_hdf5_to_model(json_filename, hdf5_filename):  \n",
    "    with open(json_filename, 'r') as f:\n",
    "        model = model_from_json(f.read())\n",
    "    model.load_weights(hdf5_filename)\n",
    "    return model\n",
    "\n",
    "def readfile(filename):\n",
    "    ids = []\n",
    "    ids_d = {}\n",
    "    seqs = {}\n",
    "    classes = {}\n",
    "    f = open(filename, 'r')\n",
    "    lines = f.readlines()\n",
    "    f.close()\n",
    "    seq = []\n",
    "    for line in lines:\n",
    "        if line[0] == '>':\n",
    "            ids.append(line[1:].rstrip('\\n'))\n",
    "            if line[1:].rstrip('\\n').split('_')[0] not in seqs:\n",
    "                seqs[line[1:].rstrip('\\n').split('_')[0]] = []\n",
    "            if line[1:].rstrip('\\n').split('_')[0] not in ids_d:\n",
    "                ids_d[line[1:].rstrip('\\n').split('_')[0]] = line[1:].rstrip('\\n').split('_')[0]\n",
    "            if line[1:].rstrip('\\n').split('_')[0] not in classes:\n",
    "                classes[line[1:].rstrip('\\n').split('_')[0]] = np.zeros(NUM_CLASSES)\n",
    "            classes[line[1:].rstrip('\\n').split('_')[0]][int(line[1:].rstrip('\\n').split('_')[1])-1] = 1        \n",
    "            if seq != []: seqs[ids[-2].split('_')[0]]= (\"\".join(seq))\n",
    "            seq = []\n",
    "        else:\n",
    "            seq.append(line.rstrip('\\n').upper())\n",
    "    if seq != []:\n",
    "        seqs[ids[-1].split('_')[0]]=(\"\".join(seq))\n",
    "\n",
    "    return ids,ids_d,seqs,classes\n",
    "\n",
    "def readfile_wolabel(filename):\n",
    "    ids = []\n",
    "    ids_d = {}\n",
    "    seqs = {}\n",
    "    f = open(filename, 'r')\n",
    "    lines = f.readlines()\n",
    "    f.close()\n",
    "    seq = []\n",
    "    for line in lines:\n",
    "        if line[0] == '>':\n",
    "            ids.append(line[1:].rstrip('\\n'))\n",
    "            if line[1:].rstrip('\\n') not in seqs:\n",
    "                seqs[line[1:].rstrip('\\n')] = []\n",
    "            if line[1:].rstrip('\\n') not in ids_d:\n",
    "                ids_d[line[1:].rstrip('\\n')] = line[1:].rstrip('\\n')    \n",
    "            if seq != []: seqs[ids[-2]]= (\"\".join(seq))\n",
    "            seq = []\n",
    "        else:\n",
    "            seq.append(line.rstrip('\\n').upper())\n",
    "    if seq != []:\n",
    "        seqs[ids[-1]]=(\"\".join(seq))\n",
    "\n",
    "    return ids,ids_d,seqs\n",
    "\n",
    "\n",
    "def one_hot_encode_along_row_axis(sequence):\n",
    "    to_return = np.zeros((1,len(sequence),4), dtype=np.int8)\n",
    "    seq_to_one_hot_fill_in_array(zeros_array=to_return[0],\n",
    "                                 sequence=sequence, one_hot_axis=1)\n",
    "    return to_return\n",
    "\n",
    "\n",
    "def seq_to_one_hot_fill_in_array(zeros_array, sequence, one_hot_axis):\n",
    "    assert one_hot_axis==0 or one_hot_axis==1\n",
    "    if (one_hot_axis==0):\n",
    "        assert zeros_array.shape[1] == len(sequence)\n",
    "    elif (one_hot_axis==1): \n",
    "        assert zeros_array.shape[0] == len(sequence)\n",
    "    for (i,char) in enumerate(sequence):\n",
    "        if (char==\"A\" or char==\"a\"):\n",
    "            char_idx = 0\n",
    "        elif (char==\"C\" or char==\"c\"):\n",
    "            char_idx = 1\n",
    "        elif (char==\"G\" or char==\"g\"):\n",
    "            char_idx = 2\n",
    "        elif (char==\"T\" or char==\"t\"):\n",
    "            char_idx = 3\n",
    "        elif (char==\"N\" or char==\"n\"):\n",
    "            continue #leave that pos as all 0's\n",
    "        else:\n",
    "            raise RuntimeError(\"Unsupported character: \"+str(char))\n",
    "        if (one_hot_axis==0):\n",
    "            zeros_array[char_idx,i] = 1\n",
    "        elif (one_hot_axis==1):\n",
    "            zeros_array[i,char_idx] = 1\n",
    "            \n",
    "def path_to_id_dict(path):\n",
    "    filename = path\n",
    "    ids, ids_d, seqs, classes = readfile_wolabel(filename)\n",
    "    result = {'ids':ids, 'ids_d':ids_d, 'seqs':seqs, 'classes':classes, }\n",
    "    return result\n",
    "\n",
    "def path_to_X_id_dict(path):\n",
    "    filename = path\n",
    "    ids, ids_d, seqs = readfile_wolabel(filename)\n",
    "    X = np.array([one_hot_encode_along_row_axis(seqs[id]) for id in ids_d]).squeeze(axis=1)\n",
    "    X_rc = [X,  X[:,::-1,::-1]]\n",
    "    result = {'ids':ids, 'ids_d':ids_d, 'seqs':seqs, 'X':X, 'X_rc':X_rc }\n",
    "    return result\n",
    "\n",
    "def path_to_X_id_dict_label(path):\n",
    "    train_filename = path   \n",
    "    train_ids,train_ids_d, train_seqs, train_classes = readfile(train_filename)\n",
    "    X = np.array([one_hot_encode_along_row_axis(train_seqs[id]) for id in train_ids_d]).squeeze(axis=1)\n",
    "    y = np.array([train_classes[id] for id in train_ids_d])\n",
    "\n",
    "    y = y[:,selected_classes]\n",
    "    X = X[y.sum(axis=1)>0]\n",
    "    ids = np.array([id for id in train_ids_d])\n",
    "    ids = ids[y.sum(axis=1)>0]\n",
    "    y = y[y.sum(axis=1)>0]\n",
    "    X_rc = [X,  X[:,::-1,::-1]]\n",
    "    \n",
    "    X_single=X[y.sum(axis=1)==1]\n",
    "    y_single=y[y.sum(axis=1)==1]\n",
    "    ids_single=ids[y.sum(axis=1)==1]\n",
    "    \n",
    "    result = {'ids':train_ids, 'ids_d':train_ids_d, 'seqs':train_seqs, 'classes':train_classes, 'X':X, 'X_rc':X_rc, 'y':y ,\"y_single\":y_single}\n",
    "    return result   \n",
    "\n",
    "def loc_to_model_loss(loc):\n",
    "    return json_hdf5_to_model(loc + 'model.json', loc + 'model_best_loss.hdf5')\n",
    "\n",
    "def shuffle_label(label):\n",
    "    for i in range(len(label.T)):\n",
    "        label.T[i] = shuffle(label.T[i])\n",
    "    return label\n",
    "\n",
    "def calculate_roc_pr(score, label):\n",
    "    output = np.zeros((len(label.T), 2))\n",
    "    for i in range(len(label.T)):\n",
    "        roc_ = roc_auc_score(label.T[i], score.T[i])\n",
    "        pr_ = average_precision_score(label.T[i], score.T[i])\n",
    "        output[i] = [roc_, pr_]\n",
    "    return output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preparing the input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq_shape = [500,4]\n",
    "NUM_CLASSES=24\n",
    "selected_classes = np.array([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24])-1\n",
    "\n",
    "train_filename = '/results/summits_to_topics.fa'\n",
    "train_ids,train_ids_d, train_seqs, train_classes = readfile(train_filename)\n",
    "X = np.array([one_hot_encode_along_row_axis(train_seqs[id]) for id in train_ids_d]).squeeze(axis=1)\n",
    "y = np.array([train_classes[id] for id in train_ids_d])\n",
    "y = y[:,selected_classes]\n",
    "X = X[y.sum(axis=1)>0]\n",
    "ids = np.array([id for id in train_ids_d])\n",
    "ids = ids[y.sum(axis=1)>0]\n",
    "y = y[y.sum(axis=1)>0]\n",
    "X_rc = [X,  X[:,::-1,::-1]]\n",
    "\n",
    "topic_dict = {\n",
    " 0: 'Topic_1 ',\n",
    " 1: 'Topic_2 ',\n",
    " 2: 'Topic_3 ',\n",
    " 3: 'Topic_4 MEL',\n",
    " 4: 'Topic_5 ',\n",
    " 5: 'Topic_6 ',\n",
    " 6: 'Topic_7 MES',\n",
    " 7: 'Topic_8 ',\n",
    " 8: 'Topic_9 ',\n",
    " 9: 'Topic_10 ',\n",
    " 10: 'Topic_11 ',\n",
    " 11: 'Topic_12 ',\n",
    " 12: 'Topic_13 ',\n",
    " 13: 'Topic_14 ',\n",
    " 14: 'Topic_15 ',\n",
    " 15: 'Topic_16 ',\n",
    " 16: 'Topic_17 ',\n",
    " 17: 'Topic_18 ',\n",
    " 18: 'Topic_19 ',\n",
    " 19: 'Topic_20 ',\n",
    " 20: 'Topic_21 ',\n",
    " 21: 'Topic_22 ',\n",
    " 22: 'Topic_23 ',\n",
    " 23: 'Topic_24 '}\n",
    "\n",
    "selected_classes_dict={}\n",
    "for i in range(len(selected_classes)):\n",
    "    selected_classes_dict[i] = 'Topic_' + str(selected_classes[i]+1)\n",
    "topic_dict_onlynum={}\n",
    "for key in topic_dict:\n",
    "    topic_dict_onlynum[key]=topic_dict[key].split(' ')[0].split('_')[1]    \n",
    "topic_list = []\n",
    "for i in range(len(selected_classes)):\n",
    "    topic_list.append(topic_dict[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading DeepMEL and initialising DeepExplainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(seed=777)\n",
    "trained_model = loc_to_model_loss('/results/')\n",
    "rn=np.random.choice(X_rc[0].shape[0], 500, replace=False)\n",
    "explainer = shap.DeepExplainer((trained_model.inputs, trained_model.layers[-1].output), [X_rc[0][rn],X_rc[1][rn]] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Performing prediction on one of the input sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "region_name = 'chr6:396135-396636'\n",
    "prediction=trained_model.predict([X_rc[0][ids==region_name],X_rc[1][ids==region_name]])[0]\n",
    "plt.plot(prediction,'--',marker=\"o\")\n",
    "plt.title(region_name)\n",
    "plt.ylabel(\"Predictions\")\n",
    "plt.xlabel(\"Topics\")\n",
    "_ = plt.xticks(range(len(selected_classes)),range(1,len(selected_classes)+1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting DeepExplainer on one of the input sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0421 18:15:36.308222 47073983102592 deprecation.py:323] From /staging/leuven/stg_00002/lcb/itask/programs/anaconda3/envs/deeplearning/lib/python3.6/site-packages/shap/explainers/deep/deep_tf.py:461: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 5760x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "region_name = 'chr6:396135-396636'\n",
    "shap_values, indexes = explainer.shap_values([X_rc[0][ids==region_name],X_rc[1][ids==region_name]], ranked_outputs=1)\n",
    "\n",
    "ntrack=3\n",
    "fig = plt.figure(figsize=(80,10))\n",
    "_, ax1 =plot_weights_modified(shap_values[0][0][0]*X_rc[0][ids==region_name],fig,ntrack,1,1,title=region_name, subticks_frequency=10,ylab=\"FORWARD\\nDeepExplainer\")\n",
    "_, ax2 =plot_weights_modified((shap_values[0][1][0]*X_rc[1][ids==region_name])[0][::-1,:],fig,ntrack,1,2,subticks_frequency=10,ylab=\"REVERSE\\nDeepExplainer\")\n",
    "ax1.set_ylim([np.min([ax1.get_ylim()[0],ax2.get_ylim()[0] ]) , np.max([ax1.get_ylim()[1],ax2.get_ylim()[1] ])])\n",
    "ax2.set_ylim([np.min([ax1.get_ylim()[0],ax2.get_ylim()[0] ]) , np.max([ax1.get_ylim()[1],ax2.get_ylim()[1] ])])\n",
    "\n",
    "arrr_A = np.zeros((len(selected_classes),X_rc[0].shape[1]))\n",
    "arrr_C = np.zeros((len(selected_classes),X_rc[0].shape[1]))\n",
    "arrr_G = np.zeros((len(selected_classes),X_rc[0].shape[1]))\n",
    "arrr_T = np.zeros((len(selected_classes),X_rc[0].shape[1]))\n",
    "new_X = np.copy(X_rc[0][ids==region_name])\n",
    "real_score=trained_model.predict([new_X,new_X[:,::-1,::-1]])[0]\n",
    "for mutloc in range(seq_shape[0]):     \n",
    "    new_X_= np.copy(new_X)\n",
    "    new_X_[0][mutloc,:] = np.array([1, 0, 0, 0], dtype='int8')\n",
    "    arrr_A[:,mutloc]=(real_score - trained_model.predict([new_X_,new_X_[:,::-1,::-1]])[0])\n",
    "    new_X_[0][mutloc,:] = np.array([0, 1, 0, 0], dtype='int8')\n",
    "    arrr_C[:,mutloc]=(real_score - trained_model.predict([new_X_,new_X_[:,::-1,::-1]])[0])\n",
    "    new_X_[0][mutloc,:] = np.array([0, 0, 1, 0], dtype='int8')\n",
    "    arrr_G[:,mutloc]=(real_score - trained_model.predict([new_X_,new_X_[:,::-1,::-1]])[0])\n",
    "    new_X_[0][mutloc,:] = np.array([0, 0, 0, 1], dtype='int8')\n",
    "    arrr_T[:,mutloc]=(real_score - trained_model.predict([new_X_,new_X_[:,::-1,::-1]])[0])\n",
    "arrr_A[arrr_A==0]=None\n",
    "arrr_C[arrr_C==0]=None\n",
    "arrr_G[arrr_G==0]=None\n",
    "arrr_T[arrr_T==0]=None\n",
    "\n",
    "ax = fig.add_subplot(ntrack,1,3)\n",
    "ax.scatter(range(X_rc[0].shape[1]),-1*arrr_A[indexes[0][0]],label='A',color='green')\n",
    "ax.scatter(range(X_rc[0].shape[1]),-1*arrr_C[indexes[0][0]],label='C',color='blue')\n",
    "ax.scatter(range(X_rc[0].shape[1]),-1*arrr_G[indexes[0][0]],label='G',color='orange')\n",
    "ax.scatter(range(X_rc[0].shape[1]),-1*arrr_T[indexes[0][0]],label='T',color='red')\n",
    "ax.legend()\n",
    "ax.axhline(y=0,linestyle='--',color='gray')\n",
    "ax.set_xlim((0,X_rc[0].shape[1])) \n",
    "_ = ax.set_xticks(np.arange(0, seq_shape[0]+1, 10))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "SSH r06i00n13 Deeplearning conda",
   "language": "python3",
   "name": "rik_ssh_r06i00n13_deeplearningconda"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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